Morning

• Arsenio: Introduction and week plan
  
• Ben: What is R? Why use R?
• Joe: The learning curve and motivation
• Arsenio: Preparing the environment

Afternoon

• Arsenio: Preparing the data (basic only, save databases, etc. for day 5)
  
• Ben: Introduction to ggplot2 (context, rationale, structure)
• Joe: First chart

• Main goals for the course
• Main goals for each day

• Why use it?
  • Intuitive, powerful, and popular.
  • Open source: it’s free and maintained and upadated by programmers all over the world.

<<<<<<< HEAD ## What is R? (Ben)

• Object oriented programming language -Designed to make data handling and statitics intuitive.

• Why use it?
  • Intuitive, powerful, and popular.
  • Open source: it’s free and maintained and upadated by programmers all over the world.

• Motivation
• Expectations
• Active, engaged learning

## Warning: Removed 450 rows containing missing values (geom_path).

• Installing R
• Installing RStudio
• Understanding the interface / the 4 panels
  • The scripts.
  • The console.
  • The global environment.
  • The plots.

For this training, we will use:

1. R – a free software environment for statistical computing and graphics (download and install from https://cloud.r-project.org/)
2. RStudio – an integrated development environment for R (download and install from https://rstudio.com/products/rstudio/download/)
3. Packages – extend the capabilities of R. To install a package, open RStudio then
   • Packages –> Install –> type the package name in Packages –> Install or
   • Type install.packages("package_name") then hit Enter key
     • e.g.: install.packages(“plotly”)

Run the following code to install all the necessary packages

• Exploring packages and functions to read in data.
• Cleaning column names, removing missing values, and formatting variables.
• Preparing the data for analysis and visualization.

• The advantages of ggplot.
• Quick, simple, and beautiful.
• Exploring the basics as well as showing the potential.

• Its part of a pipeline of well maintained packages.
• Tidyverse = readr -> dplyr -> ggplot2

## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

Different packages/functions to import data from different file formats

Swiss-army knife for data import/export: rio

Different packages for different DBMS, e.g.:

• RODBC – implements ODBC, an API for accessing DBMS
• RMySQL/RMariaDB – for accessing MySQL/MariaDB databases
• ROracle – for accessing Oracle database
• RPostgreSQL – for accessing PostgreSQL database
• RSQLite – for accessing SQLite database
• RMongo – for accessing MongoDB database
• …

E.g.: Importing data from MariaDB

• Often the most time-consuming part of any data analysis
• Some useful functions:

(Interactive activity)

• Create an object called gmx. This will be gm, but we will filter to include only the most recent year (2007).

• Create an object called gmx. This will be gm, but we will filter to include only the most recent year (2007).

• Create an object called gmx. This will be gm, but we will filter to include only the most recent year (2007).

• Make an object called ncountries. To do this, group the data by continent and tally the number of countries

• Make an object called ncountries. To do this, group the data by continent and tally the number of countries

• Arrange ncountries from lowest to highest

• Arrange ncountries from lowest to highest

• Make a plot of the continents (x-axis) and the number of countries (y-axis)

• Make a plot of the continents (x-axis) and the number of countries (y-axis)

• Make a plot of the continents (x-axis) and the number of countries (y-axis)

• Make a plot of the continents (x-axis) and the number of countries (y-axis)

• Let’s create an object called moz. This will be the gapminder data, but just for Mozambique.

• Let’s create an object called moz. This will be the gapminder data, but just for Mozambique.

• Plot GDP per capita over time

• Plot life expectancy over time

• Plot the association between gdpPercap and life expectancy

• Plot the association between gdpPercap and life expectancy

• Take the gapminder data, keep only 2007 data, group by continent, and get the maximum and minimum life expectancy for each continent

• Take the gapminder data, keep only 2007 data, group by continent, mutate a new variable with the average GDP for that continent, mutate another variable with the difference between each country and its continent’s average GDP

• How else can we visualize the gapminder data?

Morning

• Arsenio: Univariate graphs - Categorical
• Ben: Univeriate graphs - Quantitative
• Joe: Univariate graphs - Exercises

Afternoon

• Ben: Bivariate graphs - Introduction
• Arsenio: Bivariate graphs|Categorical vs Categorical
• Joe: Bivariate graphs|Quantitative vs Quantitative
• Ben: Bivariate graphs|Categorical vs Quantitative
• Arsenio: Bivariate graphs - Exercises

Morning

• Ben: Multivariate graphs - Introduction
• Joe: Multivariate graphs - Preparing the data (dplyr, grouping, etc.).
• Arsenio: Multivariate graphs - Practical instruction
• All: Multivariate graphs - Exercises

Afternoon

• Joe: Mapping - Introduction
  
• Arsenio: Mapping - Point maps / dot density maps
• Ben: Mapping - Choropleth maps
  
• All: Mapping - Exercises

Morning

• Ben: Time-dependent maps - Introduction
• Joe: Time-dependent maps - Time series
• Arsenio: Time-dependent maps - Dumbbell, slope, area charts
• All: Time-dependent maps - Exercises

Afternoon

• Arsenio: Statistical models - Introduction
• Ben: Statistical models - Correlation plots
• Arsenio: Statistical models - Linear regression
• Joe: Statistical models - Logistic regression
• Ben: Statistical models - Mosaic plots
• Ben: Statistical models - Survival plots
• All: Statistical models - Exercises

Morning

• Arsenio: Other graphs - Introduction
• Joe: Interactive graphs - Overview
• Ben: Customizing graphs - Tips
• Joe: Saving graphs - Overview
• Arsenio: Advice

Afternoon

• Visualization competition
• Extra / personal project help time

• Kabacoff, R. (2018). Data Visualization with R. QAC
• Moulik, T. (2018). Applied Data Visualization with R and Ggplot2. Packt Publishing